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In [2]:
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from pandas_profiling import ProfileReport
import pandas as pd
import numpy as np
from pandas_profiling import ProfileReport import pandas as pd import numpy as np
c:\Users\ericl\miniconda3\lib\site-packages\tqdm\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
In [55]:
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df = pd.read_csv('train.csv')
df = pd.read_csv('train.csv')
In [11]:
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df.head()
df.head()
Out[11]:
raw_row_number location county_name subject_age subject_race subject_sex officer_id_hash department_name type arrest_made ... outcome frisk_performed search_conducted search_person search_vehicle reason_for_stop raw_Ethnicity raw_Race raw_action_description date_time
0 12511107 NaN forsyth county 18.0 white male f2f6b08c97 Winston-Salem Police Department vehicular False ... citation False False False False Speed Limit Violation N W Citation Issued 2010-12-05 01:51:24
1 5439683 raleigh wake county 25.0 hispanic male 1e3fa73f20 Raleigh Police Department vehicular False ... warning False True True True Vehicle Regulatory Violation H W Verbal Warning 2005-09-25 03:40:00
2 18674698 charlotte area mecklenburg county 30.0 black female 59a754eb04 Charlotte-Mecklenburg Police Department vehicular False ... warning False False False False Speed Limit Violation N B Verbal Warning 2014-11-15 02:00:00
3 12600300 charlotte area mecklenburg county 21.0 white male 0dc507ea69 Charlotte-Mecklenburg Police Department vehicular False ... warning False False False False Vehicle Regulatory Violation N W Verbal Warning 2011-01-23 00:16:00
4 6035053 NaN durham county 38.0 black female 91822b2dfe Durham Police Department vehicular False ... citation False False False False Speed Limit Violation N B Citation Issued 2006-06-18 10:17:17

5 rows × 22 columns

In [7]:
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profile = ProfileReport(df, title="Police Stop Profiling Report")
profile
profile = ProfileReport(df, title="Police Stop Profiling Report") profile
Summarize dataset: 100%|██████████| 41/41 [06:19<00:00,  9.25s/it, Completed]                               
Generate report structure: 100%|██████████| 1/1 [00:07<00:00,  7.29s/it]
Render HTML: 100%|██████████| 1/1 [00:01<00:00,  1.73s/it]
Out[7]:




























In [56]:

        
        
        

        

            
            

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#Getting rid of unneccessary columns 
df["outcome"].replace(['warning', 'citation', 'arrest'], [0, 1, 2], inplace=True)
df.drop(columns=['date_time', 'department_name', 'county_name', 'type', 'raw_Race', 'raw_Ethnicity', 'raw_action_description', 'subject_race', 'location', 'raw_row_number', 'officer_id_hash', 'arrest_made', 'citation_issued', 'warning_issued'], inplace=True)



#Getting rid of unneccessary columns 
df["outcome"].replace(['warning', 'citation', 'arrest'], [0, 1, 2], inplace=True)
df.drop(columns=['date_time', 'department_name', 'county_name', 'type', 'raw_Race', 'raw_Ethnicity', 'raw_action_description', 'subject_race', 'location', 'raw_row_number', 'officer_id_hash', 'arrest_made', 'citation_issued', 'warning_issued'], inplace=True)

    
        

    






















In [57]:

        
        
        

        

            
            

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#Change reason for stop and boolean columns to numerical
df = pd.concat([df, df['reason_for_stop'].str.get_dummies()], axis=1)
df.drop(columns=['reason_for_stop'], inplace=True)
df['subject_sex'].replace({'male': 1, 'female': 0}, inplace=True)
df['frisk_performed'].replace({True: 1, False: 0}, inplace=True)
df['search_conducted'].replace({True: 1, False: 0}, inplace=True)
df['search_person'].replace({True: 1, False: 0}, inplace=True)
df['search_vehicle'].replace({True: 1, False: 0}, inplace=True)



#Change reason for stop and boolean columns to numerical
df = pd.concat([df, df['reason_for_stop'].str.get_dummies()], axis=1)
df.drop(columns=['reason_for_stop'], inplace=True)
df['subject_sex'].replace({'male': 1, 'female': 0}, inplace=True)
df['frisk_performed'].replace({True: 1, False: 0}, inplace=True)
df['search_conducted'].replace({True: 1, False: 0}, inplace=True)
df['search_person'].replace({True: 1, False: 0}, inplace=True)
df['search_vehicle'].replace({True: 1, False: 0}, inplace=True)

    
        

    






















In [76]:

        
        
        

        

            
            

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ProfileReport(df, title='Useful Profile Report')



ProfileReport(df, title='Useful Profile Report')

    
        

    





















    
    






Summarize dataset: 100%|██████████| 32/32 [09:45<00:00, 18.29s/it, Completed]                                     
Generate report structure: 100%|██████████| 1/1 [00:09<00:00,  9.28s/it]
Render HTML: 100%|██████████| 1/1 [00:01<00:00,  1.35s/it]










    
    
















    
    
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In [66]:

        
        
        

        

            
            

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#read test files and also perform the same changes
test = pd.read_csv('test.csv')
test["outcome"].replace(['warning', 'citation', 'arrest'], [0, 1, 2], inplace=True)
test.drop(columns=['date_time', 'department_name', 'county_name', 'type', 'raw_Race', 'raw_Ethnicity', 'raw_action_description', 'subject_race', 'location', 'raw_row_number', 'officer_id_hash', 'arrest_made', 'citation_issued', 'warning_issued'], inplace=True)
test = pd.concat([test, test['reason_for_stop'].str.get_dummies()], axis=1)
test.drop(columns=['reason_for_stop'], inplace=True)
test['subject_sex'].replace({'male': 1, 'female': 0}, inplace=True)
test['frisk_performed'].replace({True: 1, False: 0}, inplace=True)
test['search_conducted'].replace({True: 1, False: 0}, inplace=True)
test['search_person'].replace({True: 1, False: 0}, inplace=True)
test['search_vehicle'].replace({True: 1, False: 0}, inplace=True)



#read test files and also perform the same changes
test = pd.read_csv('test.csv')
test["outcome"].replace(['warning', 'citation', 'arrest'], [0, 1, 2], inplace=True)
test.drop(columns=['date_time', 'department_name', 'county_name', 'type', 'raw_Race', 'raw_Ethnicity', 'raw_action_description', 'subject_race', 'location', 'raw_row_number', 'officer_id_hash', 'arrest_made', 'citation_issued', 'warning_issued'], inplace=True)
test = pd.concat([test, test['reason_for_stop'].str.get_dummies()], axis=1)
test.drop(columns=['reason_for_stop'], inplace=True)
test['subject_sex'].replace({'male': 1, 'female': 0}, inplace=True)
test['frisk_performed'].replace({True: 1, False: 0}, inplace=True)
test['search_conducted'].replace({True: 1, False: 0}, inplace=True)
test['search_person'].replace({True: 1, False: 0}, inplace=True)
test['search_vehicle'].replace({True: 1, False: 0}, inplace=True)

    
        

    






















In [71]:

        
        
        

        

            
            

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#Seperate data for modeling
X_train = df.drop('outcome', axis=1)
X_test = test.drop('outcome', axis=1)
Y_train = df['outcome']
Y_test = test['outcome']



#Seperate data for modeling
X_train = df.drop('outcome', axis=1)
X_test = test.drop('outcome', axis=1)
Y_train = df['outcome']
Y_test = test['outcome']

    
        

    






















In [73]:

        
        
        

        

            
            

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from sklearn.linear_model import LogisticRegression

#Make the model
model = LogisticRegression(solver = 'liblinear')
model.fit(X_train,Y_train)
Y_pred = model.predict(X_test)



from sklearn.linear_model import LogisticRegression

#Make the model
model = LogisticRegression(solver = 'liblinear')
model.fit(X_train,Y_train)
Y_pred = model.predict(X_test)

    
        

    






















In [75]:

        
        
        

        

            
            

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#Accuracy
np.mean(Y_test==Y_pred)



#Accuracy
np.mean(Y_test==Y_pred)

    
        

    





















    
    
Out[75]:








0.661227829379674






























Not Very Accurate!





















In [78]:

        
        
        

        

            
            

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#Try with more relavent columns
X_train = df[['search_conducted', 'Vehicle Equipment Violation', 'Driving While Impaired', 'Safe Movement Violation', 'Speed Limit Violation']]
X_test = test[['search_conducted', 'Vehicle Equipment Violation', 'Driving While Impaired', 'Safe Movement Violation', 'Speed Limit Violation']]
Y_train = df['outcome']
Y_test = test['outcome']



#Try with more relavent columns
X_train = df[['search_conducted', 'Vehicle Equipment Violation', 'Driving While Impaired', 'Safe Movement Violation', 'Speed Limit Violation']]
X_test = test[['search_conducted', 'Vehicle Equipment Violation', 'Driving While Impaired', 'Safe Movement Violation', 'Speed Limit Violation']]
Y_train = df['outcome']
Y_test = test['outcome']

    
        

    






















In [79]:

        
        
        

        

            
            

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#Make the model
model = LogisticRegression(solver = 'liblinear')
model.fit(X_train,Y_train)
Y_pred = model.predict(X_test)

#Accuracy
np.mean(Y_test==Y_pred)



#Make the model
model = LogisticRegression(solver = 'liblinear')
model.fit(X_train,Y_train)
Y_pred = model.predict(X_test)

#Accuracy
np.mean(Y_test==Y_pred)

    
        

    





















    
    
Out[79]:








0.6585383071749138





















              
            

          

    

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